Massive sequencing has transformed the field of genome biology due to the continuous introduction and evolution of new methods. In recent years, the technologies available to read through genomes have undergone an unprecedented rate of development in terms of cost-reduction. Generating sequence data has essentially ceased to be a bottleneck for analyzing genomes instead to be replaced by limitations in sample preparation and data analysis. In this work, new strategies are presented to increase both the throughput of library generation prior to sequencing, and the informational content of libraries to aid post-sequencing data processing. The protocols developed aim to enable new possibilities for genome research concerning project scale and sequence complexity. The first two papers that underpin this thesis deal with scaling library production by means of automation. Automated library preparation is first described for the 454 sequencing system based on a generic solid-phase polyethylene-glycol precipitation protocol for automated DNA handling. This was one of the first descriptions of automated sample handling for producing next generation sequencing libraries, and substantially improved sample throughput. Building on these results, the use of a double precipitation strategy to replace the manual agarose gel excision step for Illumina sequencing is presented. This protocol considerably improved the scalability of library construction for Illumina sequencing. The third and fourth papers present advanced strategies for library tagging in order to multiplex the information available in each library. First, a dual tagging strategy for massive sequencing is described in which two sets of tags are added to a library to trace back the origins of up to 4992 amplicons using 122 tags. The tagging strategy takes advantage of the previously automated pipeline and was used for the simultaneous sequencing of 3700 amplicons. Following that, an enzymatic protocol was developed to degrade long range PCR-amplicons and forming triple-tagged libraries containing information of sample origin, clonal origin and local positioning for the short-read sequences. Through tagging, this protocol makes it possible to analyze a longer continuous sequence region than would be possible based on the read length of the sequencing system alone. The fifth study investigates commonly used enzymes for constructing libraries for massive sequencing. We analyze restriction enzymes capable of digesting unknown sequences located some distance from their recognition sequence. Some of these enzymes have previously been extensively used for massive nucleic acid analysis. In this first high throughput study of such enzymes, we investigated their restriction specificity in terms of the distance from the recognition site and their sequence dependence. The phenomenon of slippage is characterized and shown to vary significantly between enzymes. The results obtained should favor future protocol development and enzymatic understanding. Through these papers, this work aspire to aid the development of methods for massive sequencing in terms of scale, quality and knowledge; thereby contributing to the general applicability of the new paradigm of sequencing instruments. / <p>QC 20121126</p>

Abstract: Recent advances in Next-Generation Sequencing protocols have opened a variety of ways to generate data. However, each newly developed methodology is most suited to represent a certain phenomenon or molecule. The object of this analysis is to identify the most appropriate way to generate and process data to study the snoRNAs, or small nucleolar RNA. Recently, snoRNAs have been revealed as taking part in a variety of unexpected alternative functions such as splicing, resistance to oxidative shock and chromatin unwinding. Finding a method to generate and treat a large quantity of data containing snoRNAs and their potential interactors could highlight some of their unexplored roles within the cell. To tackle the problem, a new protocol was put forward. This new pipeline relies on a reverse transcriptase isolated from a bacterial group II intron which boasts a better representation of structured small RNAs such as tRNAs and snoRNAs. Indeed, when compared to data created by using the standard small RNA preparation protocol, the sequencing data generated through the group II intron retrotranscriptase gives a much fairer representation. These improvements are also present in the bioinformatics pipeline. The workflow was changed to facilitate the detection of ncRNAs. These modifications rescue millions of reads, further increasing the power of the analysis. Ultimately, such corrections increase the predictive power of sequencing data. / Des avancées récentes dans le domaine du séquençage de prochaine génération ont ouvert une panoplie de façons de générer des données. Toutefois, chaque nouvelle méthode dévelopée est souvent appropriée à la caractérisation d’un seul type de phénomène ou de molécules. L’objectif de cette analyse est d’identifier la manière la plus appropriée de générer et traiter les données pour étudier les petits ARNs nucléolaires, snoRNAs. Récemment, ceux-ci ont été révélés comme des acteurs dans une variété de fonctions alternatives comme l’épissage alternatif, la résistance au choc oxidatif et l’état de la chromatine. Il est donc impératif de trouver une méthode qui puisse traiter une large quantité de données contenant les snoRNAs et leurs intéracteurs pour découvrir les rôles encore inexplorés des snoRNAs. Dans cette optique, un nouveau protocole a été élaboré. Cette nouvelle suite d’analyses s’appuie sur une reverse transcriptase isolée d’un intron de groupe II bactérien qui affiche une meilleure représentation des petits ARNs structurés comme les tRNAs et les snoRNAs. En effet, quand les données générées à travers la méthode de préparation des libraries pour petits ARNs standard est comparée à celle basée sur la reverse transcriptase bactérienne, cette dernière donne une meilleure représentation du compte des espèces. Ces avancées sont aussi présentes dans la méthode d’analyse informatique. La suite d’outils a été modifiée afin de permettre une meilleure détection des petits ARN non-codants. Ces modifications permettent de récupérer des millions de lectures par ensemble de données ce qui augmente le pouvoir prédictif de l’analyse.

Droplet-based microfluidics is a powerful tool permitting massive-scale single-cell analysis in pico-/nano-liter water-in-oil droplets. It has been integrated into various library preparation techniques to accomplish high-throughput scRNA-seq, scDNA-seq, scATAC-seq, scChIP-seq, as well as scMulti-omics-seq. These advanced technologies have been providing unique and novel insights into both normal differentiation and disease development at single-cell level. In this thesis, we develop four new droplet-based tools for single-cell omics profiling. First, the developed Drop-BS is the first droplet-based platform to construct single-cell bisulfite sequencing libraries for DNA methylome profiling and allows production of BS library of 2,000-10,000 single cells within 2 d. We applied the technology to separately profile mixed cell lines, mouse brain tissues, and human brain tissues to reveal cell type heterogeneity. Second, the new Drop-ChIP platform only requires two steps of droplet generation to achieve multiple steps of reactions in droplets such as single-cell lysis, chromatin fragmentation, ChIP, and barcoding. Third, we aim to establish a droplet-based platform to accomplish high-throughput full-length RNA-seq (Drop-full-seq), which both current tube-based and droplet-based methods cannot realize. Last, we constructed an in-house droplet-based tool to assist single-cell ATAC-seq library preparation (Drop-ATAC), which provided a low-cost and facile protocol to conduct scATAC-seq in laboratories without the expensive instrument. / Doctor of Philosophy / Microfluidics is a collection of techniques to manipulate fluids in the micrometer scale. One of microfluidic techniques is called "droplet-based microfluidics". It can manipulate (i.e., generate, merge, sort, split, etc) pico-/nano-liter of water-in-oil droplets. First, since the water phase is separated by the continuous oil phase, these droplets are discrete and individual reactors. Second, droplet-based microfluidics can achieve highly parallel manipulation of thousands to millions of droplets. These two advantages make droplet-based microfluidics an ideal tool to perform single-cell assays. Over the past 10 years, various droplet-based platforms have been developed to study single-cell transcriptome, genome, epigenome, as well as multi-ome. To expand droplet-based tools for single-cell analysis, we aim to develop four novel platforms in this thesis. First, Drop-BS, by integrating droplet generation and droplet fusion techniques, can achieve high-throughput single-cell bisulfite sequencing library preparation. It can generate 10,000 single-cell BS libraries within 2 days which is difficult to achieve for conventional library preparation in tubes/microwells. Second, we developed a novel and facile Drop-ChIP platform to prepare single-cell ChIP-seq library. It is easy to operate since it only requires two steps of droplet generation. It also generates higher quality of data compared to previous work. In addition, we are working on the development and characterization of the other two droplet-based tools to achieve full-length single-cell RNA-seq and single-cell ATAC-seq.